The framework of generative flow networks (GFlowNets) is adopted to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference and it is demonstrated that the amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
Mingyang Zhou, Zichao Yan, Elliot Layne et al.· International Conference on...· 37 citations· ⚡3
It is discovered that the energy benefits of quantum computing economies are contingent on large-scale computation, and quantum computing may represent a more sustainable pathway for the computing industry.
Junyu Liu, Hansheng Jiang, Zuo‐Jun Max Shen· 2 citations
A new point process model for discrete-event data over networks is presented, built upon Hawkes' classic influence-kernel formulation to capture the effects of historical events on the occurrence of future events.
Zheng Dong, Matthew P. Repasky, Xiuyuan Cheng et al.· 5 citations
CON decomposition is introduced, which quantifies how much of a layer's variance each concept explains given all other concepts and the outcome, and how much none of them explains, yielding layer-comparable, calibrated scores that suppress false positives.
R. Rane, Marco Simnacher, Manuel Pfeuffer et al.· 0 citations
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This work empirically validate predictions through exact calculations and controlled stochastic optimization of the worst-case joint-distribution error permitted by excess risk at most $\varepsilon$-identifiability modulus using an $\varepsilon$-identifiability modulus.
Seq2Synth is introduced, a unified benchmark for assessing temporal and schema properties to determine applicable evaluations, covering timestamp, cross-sectional, longitudinal, and structural fidelity, alongside trajectory-aware utility and privacy.
Kiwan Kwon, Kangmin Kim, Ho-Jin Lee et al.· 0 citations
The Mamba-Assisted Closure (MAC) framework is proposed, which employs a Mamba-based sequence model to predict the closure from the resolved trajectory and couples the learned closure with the reduced-order governing equations through a numerical integrator to advance the resolved variables in time.
Zhifei Wei, S. Qadeer, Panos Stinis· arXiv.org· 0 citations
This work pairs the brain emulator with large language models that generate news headlines from linguistic parameters such as valence, arousal, and dominance and shows that these parameters can be recovered from predicted brain maps, demonstrating that the emulator's synthetic neural encodings preserve information abou...
Niels Bracher, Xavier Intes, Stefan T. Radev· arXiv.org· 0 citations
This study develops a theoretical framework framing transformers as kernel regressors, motivating a purely intrinsic strategy for ablating heads based on the stable rank of the per-head projection matrices, and uncovers the specific heads responsible for degenerate phenomena widely observed in TSFMs.
Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai et al.· arXiv.org· 3 citations
This work introduces SHAKE-GNN, a novel scalable graph-level GNN framework based on a hierarchy of Kirchhoff Forests, a class of random spanning forests used to construct stochastic multi-resolution decompositions of graphs, enabling flexible trade-offs between efficiency and performance.
The empirical evaluations on standard benchmarks and in a comparison study with widely used explanation methods show that attention weights can be meaningfully incorporated into the studied XAI frameworks, highlighting their value in enriching transformer explainability.
Marte Eggen, Jacob Lysnæs-Larsen, Inga Strümke· arXiv.org· 1 citation
The teacher, an auxiliary behavior model, is trained to sample high-loss regions of the student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum.
Minsu Kim, Sanghyeok Choi, Taeyoung Yun et al.· International Conference on...· 27 citations· ⚡6
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
